{ "item_title" : "Practical Local AI for Developers", "item_author" : [" Bevis Thibault "], "item_description" : "Practical Local AI for Developers: Run and Optimize Open-Source LLMs with Ollama, llama.cpp, and MLX to Build Private RAG Apps, AI Agents, and Offline APIsBuild powerful, private AI applications on your own hardware-without depending on expensive cloud APIs for every request.You want to run local language models, build intelligent applications, and control how your data is processed. But getting an LLM to respond on your laptop is only the beginning.Which model actually fits your RAM or GPU? Why is inference painfully slow? How do you build a reliable RAG application, connect local models to Python APIs, or prevent AI agents from performing unauthorized actions?For developers and AI engineers, the challenge isn't simply running a model. It's turning local inference into software that performs reliably, handles real documents, and works within practical hardware limitations.Without a structured approach, you can waste hours troubleshooting installations, experimenting with incompatible models, and building AI prototypes that struggle beyond basic demonstrations.Practical Local AI for Developers gives you a hands-on engineering approach to building, optimizing, testing, and operating local AI applications using tools you can control.Through progressive Python projects, practical explanations, architecture diagrams, and reproducible testing workflows, you'll move from your first local model to an integrated private AI system.Inside, you'll learn how to: Run AI models on your hardware: Set up Ollama, llama.cpp, GGUF models, and MLX-LM, with practical guidance for Windows, Linux, and Apple Silicon.Improve speed and memory efficiency: Understand quantization, GPU offloading, context management, KV-cache behavior, and inference benchmarking.Build private RAG applications: Process documents, generate local embeddings, combine semantic and keyword search, and produce source-grounded answers.Develop APIs and controlled AI agents: Create FastAPI services with streaming responses, structured outputs, persistent state, restricted tools, and approval controls.Test and operate dependable systems: Evaluate answer quality, diagnose retrieval failures, protect sensitive information, and prepare applications for repeatable deployment.Whether you're a Python developer, backend engineer, AI practitioner, or technical builder, the book emphasizes practical implementation without requiring advanced machine learning research experience.This isn't a collection of disconnected AI demonstrations. The book follows one progressive application architecture, connecting local inference, FastAPI, SQLite, Qdrant, retrieval pipelines, and controlled agent tools into a complete reference project.Across 11 chapters, you'll work with executable Python examples, complete project listings, technical architecture diagrams, troubleshooting procedures, benchmark utilities, and deterministic tests.You'll learn to distinguish a successful API response from an accurate answer, measure actual performance instead of relying on model popularity, and verify privacy boundaries rather than assuming local execution guarantees security.The result is more than knowing how to install an LLM. You'll develop the practical foundation to select suitable models, build private document assistants, integrate local AI into applications, evaluate their limitations, and maintain greater control over your AI infrastructure.Build AI applications that work on hardware you control, with performance you can measure and behavior you can test.Get your copy of Practical Local AI for Developers and start building useful, private, and reliable local AI systems.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/816/620/9798166207098_b.jpg", "price_data" : { "retail_price" : "27.00", "online_price" : "27.00", "our_price" : "27.00", "club_price" : "27.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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